Plasma-catalyzed combined dynamic wave scrubbing: A novel method for highly efficient removal of multiple pollutants from flue gas at low temperatures

Xie, Wei; Zhu, Tao; Yuan, Bo; Fu, Shunjiang; Mao, Zhishan; Ye, Zefu; Zhu, Zhujun; Zhang, Xing

doi:10.1016/j.jhazmat.2023.132518

Cited by 49 publications

(3 citation statements)

References 35 publications

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“…Artificial neural networks (ANNs) is an established machine learning technique used to forecast properties of many different types of materials, including metals, polymers, composites, and ceramics [40][41][42][43][44][45][46] . ANNs have been used to forecast the photocatalytic activity of novel catalysts [47][48][49][50][51] and to speed up the design and discovery of new catalysts [52][53][54] . Prior research has employed the gaussian process regression model to forecast the band gaps of anatase TiO 2 photocatalysts by considering their surface area and lattice properties 55 .…”

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confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

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The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

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confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

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“…, biological treatment, adsorption, ion exchange, filtration, and coagulation/flocculation) do not effectively remove antibiotic residues. 3–5 The photodegradation capability of heterogeneous catalysts in synergy with renewable solar energy utilization highlights the use of such photocatalytic systems for the detoxification of nonbiodegradable pollutants. 6,7…”

Section: Introductionmentioning

confidence: 99%

Unlocking the potential of V₂O₅ decorated on crossed g-C₃N₄ monolayers derived from synergistic bio-transformation of ZnMn₂O₄ for antibiotic photodegradation

Bahadoran,

Ajinkya,

Sharghi

et al. 2024

J. Mater. Chem. A

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“…The emission of volatile organic compounds (VOCs), primarily originating from fossil fuels and transportation activities, has led to environmental and human health issues, such as the greenhouse effect, photochemical smog, and respiratory ailments [1,2]. To address the challenges posed by VOCs, various technologies have been established, including adsorption [3], condensation [4], membrane separation [5], catalytic oxidation [6], and non-thermal plasma (NTP) [7]. Among these, NTP is considered one of the most efficient strategies, as high-energy reactive species can rapidly decompose VOCs [8,9].…”

Section: Introductionmentioning

confidence: 99%

Efficient activation of the Co/SBA-15 catalyst by high-frequency AC-DBD plasma thermal effects for toluene removal

LI 李,

JIANG 姜,

LIU 刘

et al. 2024

Plasma Sci. Technol.

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Dielectric barrier discharge (DBD) plasma excited by high-frequency alternating-current (AC) power supply is widely employed for the degradation of volatile organic compounds (VOCs). However, the thermal effect generated during the discharge process leads to energy waste and low energy utilization efficiency. In this work, an innovative DBD thermal-conducted catalysis (DBD-TCC) system, integrating high-frequency AC-DBD plasma and its generated thermal effect to activate the Co/SBA-15 catalyst, was employed for toluene removal. Specifically, Co/SBA-15 catalysts are closely positioned to the ground electrode of the plasma zone and can be heated and activated by the thermal effect when the voltage exceeds 10 kV. At 12.4 kV, the temperature in the catalyst zone could reach 261 °C in the DBD-TCC system, resulting in an increase in toluene degradation efficiency of 17%, CO2 selectivity of 21.2%, and energy efficiency of 27%, respectively, compared to the DBD system alone. In contrast, the DBD thermal-unconducted catalysis (DBD-TUC) system fails to enhance toluene degradation due to insufficient heat absorption and catalytic activation, highlighting the crucial role of AC-DBD generated heat in activating the catalyst. Furthermore, the degradation pathway and mechanism of toluene in the DBD-TCC system were hypothesized. This work is expected to provide an energy-efficiency approach for high-frequency AC-DBD plasma removal of VOCs.

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Plasma-catalyzed combined dynamic wave scrubbing: A novel method for highly efficient removal of multiple pollutants from flue gas at low temperatures

Cited by 49 publications

References 35 publications

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Unlocking the potential of V₂O₅ decorated on crossed g-C₃N₄ monolayers derived from synergistic bio-transformation of ZnMn₂O₄ for antibiotic photodegradation

Efficient activation of the Co/SBA-15 catalyst by high-frequency AC-DBD plasma thermal effects for toluene removal

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Plasma-catalyzed combined dynamic wave scrubbing: A novel method for highly efficient removal of multiple pollutants from flue gas at low temperatures

Cited by 49 publications

References 35 publications

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Unlocking the potential of V2O5 decorated on crossed g-C3N4 monolayers derived from synergistic bio-transformation of ZnMn2O4 for antibiotic photodegradation

Efficient activation of the Co/SBA-15 catalyst by high-frequency AC-DBD plasma thermal effects for toluene removal

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Unlocking the potential of V₂O₅ decorated on crossed g-C₃N₄ monolayers derived from synergistic bio-transformation of ZnMn₂O₄ for antibiotic photodegradation